Overview

Dataset statistics

Number of variables16
Number of observations21613
Missing cells0
Missing cells (%)0.0%
Duplicate rows6
Duplicate rows (%)< 0.1%
Total size in memory2.8 MiB
Average record size in memory136.0 B

Variable types

Numeric13
Categorical3

Alerts

Dataset has 6 (< 0.1%) duplicate rowsDuplicates
price is highly overall correlated with sqft_living and 2 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 1 other fieldsHigh correlation
bathrooms is highly overall correlated with bedrooms and 4 other fieldsHigh correlation
sqft_living is highly overall correlated with price and 4 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
floors is highly overall correlated with bathrooms and 1 other fieldsHigh correlation
grade is highly overall correlated with price and 4 other fieldsHigh correlation
zipcode is highly overall correlated with longHigh correlation
long is highly overall correlated with zipcodeHigh correlation
sqft_living15 is highly overall correlated with price and 3 other fieldsHigh correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
waterfront is highly overall correlated with viewHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.2%)Imbalance
sqft_basement has 13126 (60.7%) zerosZeros

Reproduction

Analysis started2023-05-11 15:35:44.807120
Analysis finished2023-05-11 15:36:20.356813
Duration35.55 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

price
Real number (ℝ)

Distinct4028
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.047817
Minimum11.225243
Maximum15.856731
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:20.496000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum11.225243
5-th percentile12.254863
Q112.682152
median13.017003
Q313.377006
95-th percentile13.960891
Maximum15.856731
Range4.6314875
Interquartile range (IQR)0.69485406

Descriptive statistics

Standard deviation0.52668452
Coefficient of variation (CV)0.04036572
Kurtosis0.69185395
Mean13.047817
Median Absolute Deviation (MAD)0.34437742
Skewness0.42807248
Sum282002.47
Variance0.27739658
MonotonicityNot monotonic
2023-05-11T08:36:20.843967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.01700286 172
 
0.8%
12.76568843 172
 
0.8%
13.21767356 159
 
0.7%
13.12236338 152
 
0.7%
12.95984445 150
 
0.7%
12.69158046 148
 
0.7%
12.89921983 145
 
0.7%
12.8346813 138
 
0.6%
12.61153775 133
 
0.6%
13.17115354 131
 
0.6%
Other values (4018) 20113
93.1%
ValueCountFrequency (%)
11.22524339 1
< 0.1%
11.26446411 1
< 0.1%
11.28978191 1
< 0.1%
11.30220443 1
< 0.1%
11.31447453 1
< 0.1%
11.32055357 1
< 0.1%
11.32659589 1
< 0.1%
11.33857208 1
< 0.1%
11.35040654 2
< 0.1%
11.36789969 1
< 0.1%
ValueCountFrequency (%)
15.85673089 1
< 0.1%
15.77030965 1
< 0.1%
15.74485569 1
< 0.1%
15.53290561 1
< 0.1%
15.49260712 1
< 0.1%
15.48321738 1
< 0.1%
15.44686651 1
< 0.1%
15.35624127 1
< 0.1%
15.31958795 1
< 0.1%
15.31714052 1
< 0.1%

bedrooms
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3708416
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:21.123545image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.93006183
Coefficient of variation (CV)0.27591383
Kurtosis49.063653
Mean3.3708416
Median Absolute Deviation (MAD)1
Skewness1.9742995
Sum72854
Variance0.86501501
MonotonicityNot monotonic
2023-05-11T08:36:21.321194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 9824
45.5%
4 6882
31.8%
2 2760
 
12.8%
5 1601
 
7.4%
6 272
 
1.3%
1 199
 
0.9%
7 38
 
0.2%
8 13
 
0.1%
0 13
 
0.1%
9 6
 
< 0.1%
Other values (3) 5
 
< 0.1%
ValueCountFrequency (%)
0 13
 
0.1%
1 199
 
0.9%
2 2760
 
12.8%
3 9824
45.5%
4 6882
31.8%
5 1601
 
7.4%
6 272
 
1.3%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 13
 
0.1%
7 38
 
0.2%
6 272
 
1.3%
5 1601
 
7.4%
4 6882
31.8%
3 9824
45.5%

bathrooms
Real number (ℝ)

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1147573
Minimum0
Maximum8
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:21.475280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.77016316
Coefficient of variation (CV)0.36418512
Kurtosis1.2799024
Mean2.1147573
Median Absolute Deviation (MAD)0.5
Skewness0.51110757
Sum45706.25
Variance0.59315129
MonotonicityNot monotonic
2023-05-11T08:36:21.750637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.5 5380
24.9%
1 3852
17.8%
1.75 3048
14.1%
2.25 2047
 
9.5%
2 1930
 
8.9%
1.5 1446
 
6.7%
2.75 1185
 
5.5%
3 753
 
3.5%
3.5 731
 
3.4%
3.25 589
 
2.7%
Other values (20) 652
 
3.0%
ValueCountFrequency (%)
0 10
 
< 0.1%
0.5 4
 
< 0.1%
0.75 72
 
0.3%
1 3852
17.8%
1.25 9
 
< 0.1%
1.5 1446
 
6.7%
1.75 3048
14.1%
2 1930
 
8.9%
2.25 2047
 
9.5%
2.5 5380
24.9%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 2
 
< 0.1%
6.25 2
 
< 0.1%
6 6
< 0.1%
5.75 4
 
< 0.1%
5.5 10
< 0.1%
5.25 13
0.1%

sqft_living
Real number (ℝ)

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2079.8997
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:22.018745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11427
median1910
Q32550
95-th percentile3760
Maximum13540
Range13250
Interquartile range (IQR)1123

Descriptive statistics

Standard deviation918.4409
Coefficient of variation (CV)0.44157941
Kurtosis5.243093
Mean2079.8997
Median Absolute Deviation (MAD)540
Skewness1.4715554
Sum44952873
Variance843533.68
MonotonicityNot monotonic
2023-05-11T08:36:22.305769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 138
 
0.6%
1400 135
 
0.6%
1440 133
 
0.6%
1010 129
 
0.6%
1660 129
 
0.6%
1800 129
 
0.6%
1820 128
 
0.6%
1480 125
 
0.6%
1720 125
 
0.6%
1540 124
 
0.6%
Other values (1028) 20318
94.0%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
8000 1
< 0.1%

sqft_lot
Real number (ℝ)

Distinct9782
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15106.968
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:22.529755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7618
Q310688
95-th percentile43339.2
Maximum1651359
Range1650839
Interquartile range (IQR)5648

Descriptive statistics

Standard deviation41420.512
Coefficient of variation (CV)2.7418151
Kurtosis285.07782
Mean15106.968
Median Absolute Deviation (MAD)2618
Skewness13.060019
Sum3.2650689 × 108
Variance1.7156588 × 109
MonotonicityNot monotonic
2023-05-11T08:36:22.827654image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 358
 
1.7%
6000 290
 
1.3%
4000 251
 
1.2%
7200 220
 
1.0%
4800 120
 
0.6%
7500 119
 
0.6%
4500 114
 
0.5%
8400 111
 
0.5%
9600 109
 
0.5%
3600 103
 
0.5%
Other values (9772) 19818
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 2
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1651359 1
< 0.1%
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
920423 1
< 0.1%
881654 1
< 0.1%
871200 2
< 0.1%
843309 1
< 0.1%

floors
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.494309
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:23.179306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5399889
Coefficient of variation (CV)0.36136361
Kurtosis-0.48472294
Mean1.494309
Median Absolute Deviation (MAD)0.5
Skewness0.61617672
Sum32296.5
Variance0.29158801
MonotonicityNot monotonic
2023-05-11T08:36:23.462258image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10680
49.4%
2 8241
38.1%
1.5 1910
 
8.8%
3 613
 
2.8%
2.5 161
 
0.7%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
1 10680
49.4%
1.5 1910
 
8.8%
2 8241
38.1%
2.5 161
 
0.7%
3 613
 
2.8%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
3.5 8
 
< 0.1%
3 613
 
2.8%
2.5 161
 
0.7%
2 8241
38.1%
1.5 1910
 
8.8%
1 10680
49.4%

waterfront
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
0
21450 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Length

2023-05-11T08:36:23.629292image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T08:36:23.788765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21613
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 21613
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 21450
99.2%
1 163
 
0.8%

view
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
0
19489 
2
 
963
3
 
510
1
 
332
4
 
319

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Length

2023-05-11T08:36:23.954314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T08:36:24.168975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21613
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 21613
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 19489
90.2%
2 963
 
4.5%
3 510
 
2.4%
1 332
 
1.5%
4 319
 
1.5%

condition
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size337.7 KiB
3
14031 
4
5679 
5
1701 
2
 
172
1
 
30

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21613
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Length

2023-05-11T08:36:24.305774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-11T08:36:24.464454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21613
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 21613
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21613
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 14031
64.9%
4 5679
26.3%
5 1701
 
7.9%
2 172
 
0.8%
1 30
 
0.1%

grade
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6568732
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:24.583787image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1754588
Coefficient of variation (CV)0.15351681
Kurtosis1.1909321
Mean7.6568732
Median Absolute Deviation (MAD)1
Skewness0.7711032
Sum165488
Variance1.3817033
MonotonicityNot monotonic
2023-05-11T08:36:24.737319image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 8981
41.6%
8 6068
28.1%
9 2615
 
12.1%
6 2038
 
9.4%
10 1134
 
5.2%
11 399
 
1.8%
5 242
 
1.1%
12 90
 
0.4%
4 29
 
0.1%
13 13
 
0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 3
 
< 0.1%
4 29
 
0.1%
5 242
 
1.1%
6 2038
 
9.4%
7 8981
41.6%
8 6068
28.1%
9 2615
 
12.1%
10 1134
 
5.2%
11 399
 
1.8%
ValueCountFrequency (%)
13 13
 
0.1%
12 90
 
0.4%
11 399
 
1.8%
10 1134
 
5.2%
9 2615
 
12.1%
8 6068
28.1%
7 8981
41.6%
6 2038
 
9.4%
5 242
 
1.1%
4 29
 
0.1%

sqft_basement
Real number (ℝ)

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.50905
Minimum0
Maximum4820
Zeros13126
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:24.966646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.57504
Coefficient of variation (CV)1.5182206
Kurtosis2.7155742
Mean291.50905
Median Absolute Deviation (MAD)0
Skewness1.5779651
Sum6300385
Variance195872.67
MonotonicityNot monotonic
2023-05-11T08:36:25.161559image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13126
60.7%
600 221
 
1.0%
700 218
 
1.0%
500 214
 
1.0%
800 206
 
1.0%
400 184
 
0.9%
1000 149
 
0.7%
900 144
 
0.7%
300 142
 
0.7%
200 108
 
0.5%
Other values (296) 6901
31.9%
ValueCountFrequency (%)
0 13126
60.7%
10 2
 
< 0.1%
20 1
 
< 0.1%
40 4
 
< 0.1%
50 11
 
0.1%
60 10
 
< 0.1%
65 1
 
< 0.1%
70 7
 
< 0.1%
80 20
 
0.1%
90 21
 
0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

zipcode
Real number (ℝ)

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.94
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:25.471262image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398118
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.505026
Coefficient of variation (CV)0.00054553579
Kurtosis-0.85347887
Mean98077.94
Median Absolute Deviation (MAD)42
Skewness0.40566121
Sum2.1197585 × 109
Variance2862.7878
MonotonicityNot monotonic
2023-05-11T08:36:26.071631image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103 602
 
2.8%
98038 590
 
2.7%
98115 583
 
2.7%
98052 574
 
2.7%
98117 553
 
2.6%
98042 548
 
2.5%
98034 545
 
2.5%
98118 508
 
2.4%
98023 499
 
2.3%
98006 498
 
2.3%
Other values (60) 16113
74.6%
ValueCountFrequency (%)
98001 362
1.7%
98002 199
 
0.9%
98003 280
1.3%
98004 317
1.5%
98005 168
 
0.8%
98006 498
2.3%
98007 141
 
0.7%
98008 283
1.3%
98010 100
 
0.5%
98011 195
 
0.9%
ValueCountFrequency (%)
98199 317
1.5%
98198 280
1.3%
98188 136
 
0.6%
98178 262
1.2%
98177 255
1.2%
98168 269
1.2%
98166 254
1.2%
98155 446
2.1%
98148 57
 
0.3%
98146 288
1.3%

lat
Real number (ℝ)

Distinct5034
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560053
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:26.287400image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.471
median47.5718
Q347.678
95-th percentile47.74964
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.207

Descriptive statistics

Standard deviation0.13856371
Coefficient of variation (CV)0.0029134474
Kurtosis-0.676313
Mean47.560053
Median Absolute Deviation (MAD)0.1049
Skewness-0.48527048
Sum1027915.4
Variance0.019199902
MonotonicityNot monotonic
2023-05-11T08:36:26.496255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.5491 17
 
0.1%
47.6846 17
 
0.1%
47.6624 17
 
0.1%
47.5322 17
 
0.1%
47.6711 16
 
0.1%
47.6886 16
 
0.1%
47.6955 16
 
0.1%
47.686 15
 
0.1%
47.6647 15
 
0.1%
47.6904 15
 
0.1%
Other values (5024) 21452
99.3%
ValueCountFrequency (%)
47.1559 1
< 0.1%
47.1593 1
< 0.1%
47.1622 1
< 0.1%
47.1647 1
< 0.1%
47.1764 1
< 0.1%
47.1775 1
< 0.1%
47.1776 2
< 0.1%
47.1795 1
< 0.1%
47.1803 1
< 0.1%
47.1808 1
< 0.1%
ValueCountFrequency (%)
47.7776 3
< 0.1%
47.7775 3
< 0.1%
47.7774 1
 
< 0.1%
47.7772 3
< 0.1%
47.7771 2
 
< 0.1%
47.777 2
 
< 0.1%
47.7769 3
< 0.1%
47.7768 2
 
< 0.1%
47.7767 6
< 0.1%
47.7766 4
< 0.1%

long
Real number (ℝ)

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2139
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21613
Negative (%)100.0%
Memory size337.7 KiB
2023-05-11T08:36:26.706307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.125
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.203

Descriptive statistics

Standard deviation0.14082834
Coefficient of variation (CV)-0.0011523104
Kurtosis1.0495009
Mean-122.2139
Median Absolute Deviation (MAD)0.101
Skewness0.88505298
Sum-2641408.9
Variance0.019832622
MonotonicityNot monotonic
2023-05-11T08:36:26.919337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29 116
 
0.5%
-122.3 111
 
0.5%
-122.362 104
 
0.5%
-122.291 100
 
0.5%
-122.372 99
 
0.5%
-122.363 99
 
0.5%
-122.288 98
 
0.5%
-122.357 96
 
0.4%
-122.284 95
 
0.4%
-122.365 94
 
0.4%
Other values (742) 20601
95.3%
ValueCountFrequency (%)
-122.519 1
 
< 0.1%
-122.515 1
 
< 0.1%
-122.514 1
 
< 0.1%
-122.512 1
 
< 0.1%
-122.511 2
< 0.1%
-122.509 2
< 0.1%
-122.507 1
 
< 0.1%
-122.506 1
 
< 0.1%
-122.505 3
< 0.1%
-122.504 2
< 0.1%
ValueCountFrequency (%)
-121.315 2
< 0.1%
-121.316 1
< 0.1%
-121.319 1
< 0.1%
-121.321 1
< 0.1%
-121.325 1
< 0.1%
-121.352 2
< 0.1%
-121.359 1
< 0.1%
-121.364 2
< 0.1%
-121.402 1
< 0.1%
-121.403 1
< 0.1%

sqft_living15
Real number (ℝ)

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1986.5525
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:27.121599image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32360
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)870

Descriptive statistics

Standard deviation685.3913
Coefficient of variation (CV)0.34501545
Kurtosis1.5970958
Mean1986.5525
Median Absolute Deviation (MAD)410
Skewness1.1081813
Sum42935359
Variance469761.24
MonotonicityNot monotonic
2023-05-11T08:36:27.291572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540 197
 
0.9%
1440 195
 
0.9%
1560 192
 
0.9%
1500 181
 
0.8%
1460 169
 
0.8%
1580 167
 
0.8%
1610 166
 
0.8%
1800 166
 
0.8%
1720 166
 
0.8%
1620 165
 
0.8%
Other values (767) 19849
91.8%
ValueCountFrequency (%)
399 1
 
< 0.1%
460 2
 
< 0.1%
620 2
 
< 0.1%
670 1
 
< 0.1%
690 2
 
< 0.1%
700 2
 
< 0.1%
710 2
 
< 0.1%
720 2
 
< 0.1%
740 8
< 0.1%
750 3
 
< 0.1%
ValueCountFrequency (%)
6210 1
 
< 0.1%
6110 1
 
< 0.1%
5790 6
< 0.1%
5610 1
 
< 0.1%
5600 1
 
< 0.1%
5500 1
 
< 0.1%
5380 1
 
< 0.1%
5340 1
 
< 0.1%
5330 1
 
< 0.1%
5220 1
 
< 0.1%

sqft_lot15
Real number (ℝ)

Distinct8689
Distinct (%)40.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12768.456
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size337.7 KiB
2023-05-11T08:36:27.468760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1999.2
Q15100
median7620
Q310083
95-th percentile37062.8
Maximum871200
Range870549
Interquartile range (IQR)4983

Descriptive statistics

Standard deviation27304.18
Coefficient of variation (CV)2.1384089
Kurtosis150.76311
Mean12768.456
Median Absolute Deviation (MAD)2505
Skewness9.5067432
Sum2.7596463 × 108
Variance7.4551823 × 108
MonotonicityNot monotonic
2023-05-11T08:36:27.656049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 427
 
2.0%
4000 357
 
1.7%
6000 289
 
1.3%
7200 211
 
1.0%
4800 145
 
0.7%
7500 142
 
0.7%
8400 116
 
0.5%
3600 111
 
0.5%
4500 111
 
0.5%
5100 109
 
0.5%
Other values (8679) 19595
90.7%
ValueCountFrequency (%)
651 1
 
< 0.1%
659 1
 
< 0.1%
660 1
 
< 0.1%
748 2
< 0.1%
750 4
< 0.1%
755 1
 
< 0.1%
757 1
 
< 0.1%
758 1
 
< 0.1%
788 1
 
< 0.1%
794 1
 
< 0.1%
ValueCountFrequency (%)
871200 1
< 0.1%
858132 1
< 0.1%
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
425581 1
< 0.1%
422967 1
< 0.1%
411962 1
< 0.1%
392040 2
< 0.1%
386812 1
< 0.1%

Interactions

2023-05-11T08:36:17.121992image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:47.457966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:50.397677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:52.696057image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:54.968548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:57.268582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:59.201096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:01.461096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:03.316097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:05.436685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:09.343049image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:11.418766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:14.459866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:17.305280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:48.512990image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:50.545207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:52.857633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:55.107205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:57.405651image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:59.329077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:01.587887image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:03.460717image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:05.769382image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:09.497015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:11.552250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:14.603151image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:17.570436image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:48.664951image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:50.698721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:53.024249image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:55.270529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:57.545915image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:59.466018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:01.730582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:03.626633image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:06.156367image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:09.642846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:11.693572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:14.836077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:17.805214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:48.814692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:50.837333image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:53.260912image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:55.434204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:57.687970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:59.601760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:01.866243image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:03.791713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:06.492839image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:09.798577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:11.854657image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:14.987355image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:18.047173image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:48.964314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:50.976653image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:53.402684image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:55.610066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:57.848768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:59.739661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:02.006897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:03.952579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:07.021149image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:09.962570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:12.004932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:15.183459image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:18.276816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:49.112752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:51.105703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:53.564624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:55.909401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:58.006178image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:59.869841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:02.135160image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:04.107009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:07.258250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:10.120337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:12.180669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:15.327090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:18.478214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:49.254316image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:51.236692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:53.718207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:56.075021image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:58.144862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:00.001325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:02.268237image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:04.250373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:07.510482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:10.262073image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:12.406572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:15.486261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:18.727933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:49.407732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:51.518195image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:53.873463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:56.239393image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:58.273869image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:00.129051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:02.404661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:04.387357image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:07.739058image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:10.410493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:12.638679image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:15.710761image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:18.915101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:49.553346image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:51.725792image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:54.039207image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:56.400649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:58.419162image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:00.281543image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:02.549122image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:04.530304image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:08.060989image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:10.578525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:12.873991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:15.966977image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:19.178791image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:49.768785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:52.044574image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:54.303649image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:56.625614image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:58.627144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:00.658255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:02.761560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:04.736435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:08.419371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:10.807866image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:13.187795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:16.181142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:19.341216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:49.946665image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:52.236548image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:54.500132image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:56.782680image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:58.779557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:00.789527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:02.911042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:04.916044image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:08.641865image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:10.959236image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:13.414067image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:16.460492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:19.508591image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:50.104775image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:52.403598image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:54.660799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:56.978470image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:58.938152image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:00.972667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:03.050864image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:05.048897image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:08.894014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:11.119923image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:13.761806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:16.662676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:19.662492image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:50.253587image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:52.570366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:54.804438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:57.122340image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:35:59.073072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:01.280413image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:03.181703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:05.233488image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:09.134898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:11.262494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:13.985422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-05-11T08:36:16.930847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-05-11T08:36:27.813016image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
pricebedroomsbathroomssqft_livingsqft_lotfloorsgradesqft_basementzipcodelatlongsqft_living15sqft_lot15waterfrontviewcondition
price1.0000.3450.4970.6440.0750.3220.6580.252-0.0090.4560.0640.5720.0630.3110.2250.088
bedrooms0.3451.0000.5210.6470.2170.2280.3810.230-0.167-0.0210.1910.4440.2020.0000.0380.024
bathrooms0.4970.5211.0000.7460.0690.5470.6580.192-0.2050.0080.2620.5700.0630.1020.1140.130
sqft_living0.6440.6470.7461.0000.3040.4010.7160.328-0.2070.0310.2850.7470.2840.1400.1490.060
sqft_lot0.0750.2170.0690.3041.000-0.2340.1520.037-0.319-0.1220.3710.3600.9220.0140.0400.039
floors0.3220.2280.5470.401-0.2341.0000.502-0.272-0.0610.0250.1490.305-0.2310.0220.0240.179
grade0.6580.3810.6580.7160.1520.5021.0000.093-0.1820.1040.2230.6630.1560.1180.1430.154
sqft_basement0.2520.2300.1920.3280.037-0.2720.0931.0000.1150.116-0.2000.1300.0310.1340.1590.094
zipcode-0.009-0.167-0.205-0.207-0.319-0.061-0.1820.1151.0000.250-0.577-0.287-0.3260.2030.1620.155
lat0.456-0.0210.0080.031-0.1220.0250.1040.1160.2501.000-0.1430.028-0.1170.0340.0680.058
long0.0640.1910.2620.2850.3710.1490.223-0.200-0.577-0.1431.0000.3800.3730.0960.0850.081
sqft_living150.5720.4440.5700.7470.3600.3050.6630.130-0.2870.0280.3801.0000.3660.0890.1470.062
sqft_lot150.0630.2020.0630.2840.922-0.2310.1560.031-0.326-0.1170.3730.3661.0000.0000.0350.013
waterfront0.3110.0000.1020.1400.0140.0220.1180.1340.2030.0340.0960.0890.0001.0000.5920.017
view0.2250.0380.1140.1490.0400.0240.1430.1590.1620.0680.0850.1470.0350.5921.0000.025
condition0.0880.0240.1300.0600.0390.1790.1540.0940.1550.0580.0810.0620.0130.0170.0251.000

Missing values

2023-05-11T08:36:19.897674image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-11T08:36:20.154034image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

pricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_basementzipcodelatlongsqft_living15sqft_lot15
012.30998231.00118056501.0003709817847.5112-122.25713405650
113.19561432.25257072422.000374009812547.7210-122.31916907639
212.10071221.00770100001.0003609802847.7379-122.23327208062
313.31132943.00196050001.000579109813647.5208-122.39313605000
413.14216632.00168080801.0003809807447.6168-122.04518007503
514.01845144.5054201019301.00031115309805347.6561-122.0054760101930
612.45877532.25171568192.0003709800347.3097-122.32722386819
712.58399531.50106097111.0003709819847.4095-122.31516509711
812.34365831.00178074701.000377309814647.5123-122.33717808113
912.68540832.50189065602.0003709803847.3684-122.03123907570
pricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_basementzipcodelatlongsqft_living15sqft_lot15
2160313.13675932.50227055362.0003809806547.5389-121.88122705731
2160412.96921232.00149011263.0003809814447.5699-122.28814001230
2160513.32233742.50252060232.0003909805647.5137-122.16725206023
2160613.82298343.50351072002.000399109813647.5537-122.39820506200
2160713.07107032.50131012942.000381309811647.5773-122.40913301265
2160812.79385932.50153011313.0003809810347.6993-122.34615301509
2160912.89922042.50231058132.0003809814647.5107-122.36218307200
2161012.90445920.75102013502.0003709814447.5944-122.29910202007
2161112.89922032.50160023882.0003809802747.5345-122.06914101287
2161212.69158020.75102010762.0003709814447.5941-122.29910201357

Duplicate rows

Most frequently occurring

pricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_basementzipcodelatlongsqft_living15sqft_lot15# duplicates
012.46824522.0010706492.000393509810647.5213-122.35710709282
113.17968832.2514109053.0003909811647.5818-122.402151013522
213.21767441.75241084472.003483509807447.6499-122.0882520147892
313.22672332.50194032112.0003809802747.5644-122.093188030782
413.27936732.50229050892.0003909800647.5443-122.172229079842
513.36138033.00223014072.500383809802747.5446-122.017229014072